Applyingml

Overview

The applyingml repository for applyingml.com offers a collection of papers, resources, and mentor interviews aimed at guiding individuals on how to apply machine learning techniques. Created by @eugeneyan, this project serves as a valuable resource for those looking to deepen their understanding in the field of machine learning using real-world examples and insights.

Features

• Papers: Access a curated collection of papers for reference and research.
• Resources: Explore various resources to enhance knowledge and skills in machine learning.
• Mentor Interviews: Gain valuable insights from mentor interviews on practical applications of machine learning.
• Quickstart Guide: Get started easily with a guide on how to contribute and make the most of the repository.
• Contributing: Learn how to contribute to the project by following the provided guidelines.
• Template: Utilize the interview template for mentor interviews to structure your responses effectively.
• Built with Gatsby: The project is bootstrapped with Gatsby’s hello-world starter for efficient development.
• Deployment: The project is built and deployed on push to main via Gatsby Publish for seamless updates.

Installation

To contribute to the applyingml repository and access its features, follow these steps:

1. Clone the repository:

git clone https://github.com/applyingml.git

2. Navigate to the project directory:

cd applyingml

3. Install dependencies:

npm install

4. Make your changes and contributions to the repository.
5. Push your changes:

git push origin main

6. Create a pull request with your changes for review and incorporation into the project.

Summary

The applyingml repository is a valuable resource for individuals seeking to learn and apply machine learning concepts in real-world scenarios. With a collection of papers, resources, and mentor interviews, this project offers a comprehensive guide for those looking to deepen their understanding of machine learning techniques. The easy-to-follow contributing guidelines and built-in templates make it simple for users to engage with the content and contribute to the repository, fostering a collaborative learning environment.